

import warnings
from typing import List, Optional, Union

from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding, PaddingStrategy, PreTokenizedInput, TextInput, TruncationStrategy
from ...utils import TensorType







class 					__lowercase  (						_SCREAMING_SNAKE_CASE				):





							"""simple docstring"""

							UpperCAmelCase_						:		Dict								=	['''image_processor''', '''tokenizer''']
							UpperCAmelCase_						:		int								=	'''LayoutLMv2ImageProcessor'''
							UpperCAmelCase_						:		List[Any]								=	('''LayoutXLMTokenizer''', '''LayoutXLMTokenizerFast''')




							def __init__(					self    ,						__UpperCAmelCase=None    ,						__UpperCAmelCase=None    ,						**__UpperCAmelCase						)		->       str:
														if "feature_extractor" in kwargs:
																					warnings.warn(
																					    '''The `feature_extractor` argument is deprecated and will be removed in v5, use `image_processor`'''
																					    ''' instead.'''    ,						__UpperCAmelCase    ,						)
																					A      :					Dict      		=					kwargs.pop('''feature_extractor'''						)

														A      :					Optional[int]      		=					image_processor if image_processor is not None else feature_extractor
														if image_processor is None:
																					raise ValueError('''You need to specify an `image_processor`.'''						)
														if tokenizer is None:
																					raise ValueError('''You need to specify a `tokenizer`.'''						)

														super().__init__(__UpperCAmelCase    ,						__UpperCAmelCase						)




							def __call__(					self    ,						__UpperCAmelCase    ,						__UpperCAmelCase = None    ,						__UpperCAmelCase = None    ,						__UpperCAmelCase = None    ,						__UpperCAmelCase = None    ,						__UpperCAmelCase = True    ,						__UpperCAmelCase = False    ,						__UpperCAmelCase = None    ,						__UpperCAmelCase = None    ,						__UpperCAmelCase = 0    ,						__UpperCAmelCase = None    ,						__UpperCAmelCase = None    ,						__UpperCAmelCase = None    ,						__UpperCAmelCase = False    ,						__UpperCAmelCase = False    ,						__UpperCAmelCase = False    ,						__UpperCAmelCase = False    ,						__UpperCAmelCase = True    ,						__UpperCAmelCase = None    ,						**__UpperCAmelCase    ,						)		->       BatchEncoding:
														# verify input
														if self.image_processor.apply_ocr and (boxes is not None):
																					raise ValueError(
																					    '''You cannot provide bounding boxes '''
																					    '''if you initialized the image processor with apply_ocr set to True.'''						)

														if self.image_processor.apply_ocr and (word_labels is not None):
																					raise ValueError(
																					    '''You cannot provide word labels if you initialized the image processor with apply_ocr set to True.'''						)

														if return_overflowing_tokens is True and return_offsets_mapping is False:
																					raise ValueError('''You cannot return overflowing tokens without returning the offsets mapping.'''						)

														# first, apply the image processor
														A      :					List[Any]      		=					self.image_processor(images=__UpperCAmelCase    ,						return_tensors=__UpperCAmelCase						)

														# second, apply the tokenizer
														if text is not None and self.image_processor.apply_ocr and text_pair is None:
																					if isinstance(__UpperCAmelCase    ,						__UpperCAmelCase						):
																												A      :					Optional[int]      		=					[text]  # add batch dimension (as the image processor always adds a batch dimension)
																					A      :					Tuple      		=					features['''words''']

														A      :					Optional[int]      		=					self.tokenizer(
														    text=text if text is not None else features['''words''']    ,						text_pair=text_pair if text_pair is not None else None    ,						boxes=boxes if boxes is not None else features['''boxes''']    ,						word_labels=__UpperCAmelCase    ,						add_special_tokens=__UpperCAmelCase    ,						padding=__UpperCAmelCase    ,						truncation=__UpperCAmelCase    ,						max_length=__UpperCAmelCase    ,						stride=__UpperCAmelCase    ,						pad_to_multiple_of=__UpperCAmelCase    ,						return_token_type_ids=__UpperCAmelCase    ,						return_attention_mask=__UpperCAmelCase    ,						return_overflowing_tokens=__UpperCAmelCase    ,						return_special_tokens_mask=__UpperCAmelCase    ,						return_offsets_mapping=__UpperCAmelCase    ,						return_length=__UpperCAmelCase    ,						verbose=__UpperCAmelCase    ,						return_tensors=__UpperCAmelCase    ,						**__UpperCAmelCase    ,						)

														# add pixel values
														A      :					Optional[Any]      		=					features.pop('''pixel_values'''						)
														if return_overflowing_tokens is True:
																					A      :					Tuple      		=					self.get_overflowing_images(__UpperCAmelCase    ,						encoded_inputs['''overflow_to_sample_mapping''']						)
														A      :					Union[str, Any]      		=					images

														return encoded_inputs




							def       snake_case			(					self    ,						__UpperCAmelCase    ,						__UpperCAmelCase						)		->       Dict:
														# in case there's an overflow, ensure each `input_ids` sample is mapped to its corresponding image
														A      :					Optional[int]      		=					[]
														for sample_idx in overflow_to_sample_mapping:
																					images_with_overflow.append(images[sample_idx]						)

														if len(__UpperCAmelCase						) != len(__UpperCAmelCase						):
																					raise ValueError(
																					    '''Expected length of images to be the same as the length of `overflow_to_sample_mapping`, but got'''
																					    f' {len(__UpperCAmelCase						)} and {len(__UpperCAmelCase						)}'						)

														return images_with_overflow




							def       snake_case			(					self    ,						*__UpperCAmelCase    ,						**__UpperCAmelCase						)		->       str:
														return self.tokenizer.batch_decode(*__UpperCAmelCase    ,						**__UpperCAmelCase						)




							def       snake_case			(					self    ,						*__UpperCAmelCase    ,						**__UpperCAmelCase						)		->       Any:
														return self.tokenizer.decode(*__UpperCAmelCase    ,						**__UpperCAmelCase						)




							@property
							def       snake_case			(					self						)		->       Any:
														return ["input_ids", "bbox", "attention_mask", "image"]




							@property
							def       snake_case			(					self						)		->       int:
														warnings.warn(
														    '''`feature_extractor_class` is deprecated and will be removed in v5. Use `image_processor_class` instead.'''    ,						__UpperCAmelCase    ,						)
														return self.image_processor_class






							@property
							def       snake_case			(					self						)		->       Optional[Any]:
														warnings.warn(
														    '''`feature_extractor` is deprecated and will be removed in v5. Use `image_processor` instead.'''    ,						__UpperCAmelCase    ,						)
														return self.image_processor









import os
import unittest

from huggingface_hub.utils import are_progress_bars_disabled

import transformers.models.bart.tokenization_bart
from transformers import logging
from transformers.testing_utils import CaptureLogger, mockenv, mockenv_context
from transformers.utils.logging import disable_progress_bar, enable_progress_bar







class 					__lowercase  (						unittest.TestCase				):





							"""simple docstring"""

							def       snake_case			(					self						)		->       Union[str, Any]:
														A      :					Dict      		=					logging.get_logger()

														# the current default level is logging.WARNING
														A      :					List[Any]      		=					logging.get_verbosity()

														logging.set_verbosity_error()
														self.assertEqual(logger.getEffectiveLevel()    ,						logging.get_verbosity()						)

														logging.set_verbosity_warning()
														self.assertEqual(logger.getEffectiveLevel()    ,						logging.get_verbosity()						)

														logging.set_verbosity_info()
														self.assertEqual(logger.getEffectiveLevel()    ,						logging.get_verbosity()						)

														logging.set_verbosity_debug()
														self.assertEqual(logger.getEffectiveLevel()    ,						logging.get_verbosity()						)

														# restore to the original level
														logging.set_verbosity(__UpperCAmelCase						)




							def       snake_case			(					self						)		->       str:
														A      :					Any      		=					logging.get_verbosity()

														A      :					Optional[Any]      		=					logging.get_logger('''transformers.models.bart.tokenization_bart'''						)
														A      :					Tuple      		=					'''Testing 1, 2, 3'''

														# should be able to log warnings (if default settings weren't overridden by `pytest --log-level-all`)
														if level_origin <= logging.WARNING:
																					with CaptureLogger(__UpperCAmelCase						) as cl:
																												logger.warning(__UpperCAmelCase						)
																					self.assertEqual(cl.out    ,						msg + '''\n'''						)

														# this is setting the level for all of `transformers.*` loggers
														logging.set_verbosity_error()

														# should not be able to log warnings
														with CaptureLogger(__UpperCAmelCase						) as cl:
																					logger.warning(__UpperCAmelCase						)
														self.assertEqual(cl.out    ,						''''''						)

														# should be able to log warnings again
														logging.set_verbosity_warning()
														with CaptureLogger(__UpperCAmelCase						) as cl:
																					logger.warning(__UpperCAmelCase						)
														self.assertEqual(cl.out    ,						msg + '''\n'''						)

														# restore to the original level
														logging.set_verbosity(__UpperCAmelCase						)




							@mockenv(TRANSFORMERS_VERBOSITY='''error'''						)
							def       snake_case			(					self						)		->       Optional[int]:
														# reset for the env var to take effect, next time some logger call is made
														transformers.utils.logging._reset_library_root_logger()
														# this action activates the env var
														A      :					int      		=					logging.get_logger('''transformers.models.bart.tokenization_bart'''						)

														A      :					Any      		=					os.getenv('''TRANSFORMERS_VERBOSITY'''    ,						__UpperCAmelCase						)
														A      :					List[str]      		=					logging.log_levels[env_level_str]

														A      :					Optional[int]      		=					logging.get_verbosity()
														self.assertEqual(
														    __UpperCAmelCase    ,						__UpperCAmelCase    ,						f'TRANSFORMERS_VERBOSITY={env_level_str}/{env_level}, but internal verbosity is {current_level}'    ,						)

														# restore to the original level
														A      :					str      		=					''''''
														transformers.utils.logging._reset_library_root_logger()




							@mockenv(TRANSFORMERS_VERBOSITY='''super-error'''						)
							def       snake_case			(					self						)		->       Optional[int]:
														# reset for the env var to take effect, next time some logger call is made
														transformers.utils.logging._reset_library_root_logger()
														A      :					str      		=					logging.logging.getLogger()
														with CaptureLogger(__UpperCAmelCase						) as cl:
																					# this action activates the env var
																					logging.get_logger('''transformers.models.bart.tokenization_bart'''						)
														self.assertIn('''Unknown option TRANSFORMERS_VERBOSITY=super-error'''    ,						cl.out						)

														# no need to restore as nothing was changed






							def       snake_case			(					self						)		->       Optional[int]:
														# testing `logger.warning_advice()`
														transformers.utils.logging._reset_library_root_logger()

														A      :					Union[str, Any]      		=					logging.get_logger('''transformers.models.bart.tokenization_bart'''						)
														A      :					Optional[int]      		=					'''Testing 1, 2, 3'''

														with mockenv_context(TRANSFORMERS_NO_ADVISORY_WARNINGS='''1'''						):
																					# nothing should be logged as env var disables this method
																					with CaptureLogger(__UpperCAmelCase						) as cl:
																												logger.warning_advice(__UpperCAmelCase						)
																					self.assertEqual(cl.out    ,						''''''						)

														with mockenv_context(TRANSFORMERS_NO_ADVISORY_WARNINGS=''''''						):
																					# should log normally as TRANSFORMERS_NO_ADVISORY_WARNINGS is unset
																					with CaptureLogger(__UpperCAmelCase						) as cl:
																												logger.warning_advice(__UpperCAmelCase						)
																					self.assertEqual(cl.out    ,						msg + '''\n'''						)






def        snake_case__		(    ):
							disable_progress_bar()
							assert are_progress_bars_disabled()

							enable_progress_bar()
							assert not are_progress_bars_disabled()






